Dr. Maksim Tsvetovat received his Ph.D. from Carnegie Mellon University's School of Computer Science, with concentration on computational modeling of organizations. Prior to starting his Ph.D., Maksim received an M.S. in Computer Science from University of Minnesota and spent several years in the industry as a software architect and consultant in the fields of artificial intelligence and electronic commerce.
Maksim's dissertation entitled "Simulation of Social Structure using Artificial Intelligence Techniques" centered on the study of organizational change under environmental uncertainty and in the presence of hostile action. This work was accomplished by using agent-based models informed via empirical and theoretical social network analysis, while the agents themselves were powered by AI-based planning and reasoning algorithms.
When he arrived at George Mason University, Maksim continued his work in modeling organizational change, as well as broader questions of evolution of social networks and social structure and applied SNA. Empirical/applied projects included studying campaign finance and its influence on election outcomes, identification of influential individual in online social networks (e.g. Twitter), and study of emergent social-semantic systems (i.e. folksonomies and folk-ontologies) as systems of norms of understanding and participation.
His research branched out by collaborating with the Department of Neuroscience. The research involved mapping out low-level neuro-cognitive and social-reward pathways that lead to co-evolution of advanced social structure in parallel cultural and language diversity. A hypothesis was concluded that these features of humanity have co-evolved with the human brain, specifically the pre-frontal cortex (PFC) and are regulated and managed by neurotransmitter and neuropeptide circuits (e.g. dopamine, oxytocin and cortizol).